Overview

Dataset statistics

Number of variables26
Number of observations57088
Missing cells85675
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 MiB
Average record size in memory208.0 B

Variable types

Categorical13
Numeric12
Unsupported1

Alerts

targetRelease has constant value "AIR" Constant
CONTINENT has constant value "EUROPE" Constant
EPRTRAnnexIMainActivityLabel has a high cardinality: 70 distinct values High cardinality
FacilityInspireID has a high cardinality: 7184 distinct values High cardinality
facilityName has a high cardinality: 7929 distinct values High cardinality
City has a high cardinality: 5135 distinct values High cardinality
REPORTER NAME has a high cardinality: 45014 distinct values High cardinality
CITY ID has a high cardinality: 5135 distinct values High cardinality
EPRTRAnnexIMainActivityCode has a high cardinality: 70 distinct values High cardinality
max_wind_speed is highly correlated with avg_wind_speed and 1 other fieldsHigh correlation
avg_wind_speed is highly correlated with max_wind_speed and 1 other fieldsHigh correlation
min_wind_speed is highly correlated with max_wind_speed and 1 other fieldsHigh correlation
max_temp is highly correlated with avg_temp and 1 other fieldsHigh correlation
avg_temp is highly correlated with max_temp and 1 other fieldsHigh correlation
min_temp is highly correlated with max_temp and 1 other fieldsHigh correlation
max_wind_speed is highly correlated with avg_wind_speed and 1 other fieldsHigh correlation
avg_wind_speed is highly correlated with max_wind_speed and 1 other fieldsHigh correlation
min_wind_speed is highly correlated with max_wind_speed and 1 other fieldsHigh correlation
max_temp is highly correlated with avg_temp and 1 other fieldsHigh correlation
avg_temp is highly correlated with max_temp and 1 other fieldsHigh correlation
min_temp is highly correlated with max_temp and 1 other fieldsHigh correlation
max_wind_speed is highly correlated with avg_wind_speedHigh correlation
avg_wind_speed is highly correlated with max_wind_speed and 1 other fieldsHigh correlation
min_wind_speed is highly correlated with avg_wind_speedHigh correlation
max_temp is highly correlated with avg_temp and 1 other fieldsHigh correlation
avg_temp is highly correlated with max_temp and 1 other fieldsHigh correlation
min_temp is highly correlated with max_temp and 1 other fieldsHigh correlation
EPRTRAnnexIMainActivityLabel is highly correlated with CONTINENT and 5 other fieldsHigh correlation
CONTINENT is highly correlated with EPRTRAnnexIMainActivityLabel and 6 other fieldsHigh correlation
pollutant_code is highly correlated with EPRTRAnnexIMainActivityLabel and 4 other fieldsHigh correlation
eprtrSectorName is highly correlated with EPRTRAnnexIMainActivityLabel and 3 other fieldsHigh correlation
targetRelease is highly correlated with EPRTRAnnexIMainActivityLabel and 6 other fieldsHigh correlation
countryName is highly correlated with CONTINENT and 1 other fieldsHigh correlation
EPRTRAnnexIMainActivityCode is highly correlated with EPRTRAnnexIMainActivityLabel and 5 other fieldsHigh correlation
pollutant is highly correlated with EPRTRAnnexIMainActivityLabel and 4 other fieldsHigh correlation
countryName is highly correlated with EPRTRAnnexIMainActivityLabel and 3 other fieldsHigh correlation
eprtrSectorName is highly correlated with EPRTRAnnexIMainActivityLabel and 4 other fieldsHigh correlation
EPRTRAnnexIMainActivityLabel is highly correlated with countryName and 6 other fieldsHigh correlation
pollutant is highly correlated with eprtrSectorName and 4 other fieldsHigh correlation
MONTH is highly correlated with max_temp and 2 other fieldsHigh correlation
max_wind_speed is highly correlated with avg_wind_speed and 1 other fieldsHigh correlation
avg_wind_speed is highly correlated with max_wind_speed and 1 other fieldsHigh correlation
min_wind_speed is highly correlated with max_wind_speed and 1 other fieldsHigh correlation
max_temp is highly correlated with MONTH and 2 other fieldsHigh correlation
avg_temp is highly correlated with MONTH and 2 other fieldsHigh correlation
min_temp is highly correlated with MONTH and 2 other fieldsHigh correlation
DAY WITH FOGS is highly correlated with countryName and 1 other fieldsHigh correlation
EPRTRSectorCode is highly correlated with eprtrSectorName and 4 other fieldsHigh correlation
EPRTRAnnexIMainActivityCode is highly correlated with countryName and 6 other fieldsHigh correlation
Unnamed: 23 is highly correlated with countryName and 3 other fieldsHigh correlation
pollutant_code is highly correlated with eprtrSectorName and 4 other fieldsHigh correlation
Unnamed: 23 has 28587 (50.1%) missing values Missing
Unnamed: 0 has 57088 (100.0%) missing values Missing
REPORTER NAME is uniformly distributed Uniform
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysis Unsupported
DAY WITH FOGS has 16355 (28.6%) zeros Zeros

Reproduction

Analysis started2022-05-21 16:48:54.619528
Analysis finished2022-05-21 16:49:35.273197
Duration40.65 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

countryName
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
United Kingdom
7862 
Germany
7602 
France
6372 
Spain
6146 
Italy
5480 
Other values (27)
23626 

Length

Max length14
Median length7
Mean length7.584606222
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowFrance
3rd rowLithuania
4th rowItaly
5th rowRomania

Common Values

ValueCountFrequency (%)
United Kingdom7862
13.8%
Germany7602
13.3%
France6372
11.2%
Spain6146
10.8%
Italy5480
9.6%
Poland3683
 
6.5%
Netherlands2020
 
3.5%
Finland1958
 
3.4%
Sweden1843
 
3.2%
Romania1627
 
2.8%
Other values (22)12495
21.9%

Length

2022-05-21T18:49:35.398345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united7862
12.1%
kingdom7862
12.1%
germany7602
11.7%
france6372
9.8%
spain6146
9.5%
italy5480
 
8.4%
poland3683
 
5.7%
netherlands2020
 
3.1%
finland1958
 
3.0%
sweden1843
 
2.8%
Other values (23)14122
21.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

eprtrSectorName
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
Energy sector
21386 
Waste and wastewater management
13813 
Mineral industry
8922 
Chemical industry
3744 
Paper and wood production and processing
3299 
Other values (4)
5924 

Length

Max length63
Median length16
Mean length22.76585272
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMineral industry
2nd rowMineral industry
3rd rowMineral industry
4th rowMineral industry
5th rowMineral industry

Common Values

ValueCountFrequency (%)
Energy sector21386
37.5%
Waste and wastewater management13813
24.2%
Mineral industry8922
15.6%
Chemical industry3744
 
6.6%
Paper and wood production and processing3299
 
5.8%
Production and processing of metals2748
 
4.8%
Intensive livestock production and aquaculture1848
 
3.2%
Animal and vegetable products from the food and beverage sector1120
 
2.0%
Other activities208
 
0.4%

Length

2022-05-21T18:49:35.531770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T18:49:35.622872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
and27247
15.3%
sector22506
12.7%
energy21386
12.0%
waste13813
7.8%
wastewater13813
7.8%
management13813
7.8%
industry12666
7.1%
mineral8922
 
5.0%
production7895
 
4.4%
processing6047
 
3.4%
Other values (17)29638
16.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EPRTRAnnexIMainActivityLabel
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
Thermal power stations and other combustion installations
18780 
Landfills (excluding landfills of inert waste and landfills, which were definitely closed before 16.7.2001 or for which the after-care phase required by the competent authorities according to Article 13 of Council Directive 1999/31/EC of 26 April 1999 on the landfill of waste has expired)
9028 
Installations for the incineration of non-hazardous waste in the scope of Directive 2000/76/EC of the European Parliament and of the Council of 4 December 2000 on the incineration of waste
3039 
Installations for the production of cement clinker in rotary kilns
2907 
Installations for the manufacture of glass, including glass fibre
2367 
Other values (65)
20967 

Length

Max length289
Median length66
Mean length126.1144549
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInstallations for the production of cement clinker in rotary kilns
2nd rowInstallations for the production of cement clinker in rotary kilns
3rd rowInstallations for the production of cement clinker in rotary kilns
4th rowInstallations for the production of cement clinker in rotary kilns
5th rowInstallations for the production of cement clinker in rotary kilns

Common Values

ValueCountFrequency (%)
Thermal power stations and other combustion installations18780
32.9%
Landfills (excluding landfills of inert waste and landfills, which were definitely closed before 16.7.2001 or for which the after-care phase required by the competent authorities according to Article 13 of Council Directive 1999/31/EC of 26 April 1999 on the landfill of waste has expired)9028
15.8%
Installations for the incineration of non-hazardous waste in the scope of Directive 2000/76/EC of the European Parliament and of the Council of 4 December 2000 on the incineration of waste3039
 
5.3%
Installations for the production of cement clinker in rotary kilns2907
 
5.1%
Installations for the manufacture of glass, including glass fibre2367
 
4.1%
Mineral oil and gas refineries2110
 
3.7%
Industrial plants for the production of paper and board and other primary wood products (such as chipboard, fibreboard and plywood)2072
 
3.6%
Installations for the production of cement clinker in rotary kilns, lime in rotary kilns, cement or lime in other furnaces. Note to reporters, use Level 3 activity e.g. 3(c)(i), in preference to 3(c). Level 2 activity class (i.e. 3(c)) only to be used where Level 3 is not available.1326
 
2.3%
Installations for the production of pig iron or steel (primary or secondary melting) including continuous casting1268
 
2.2%
Industrial plants for the production of pulp from timber or similar fibrous materials1221
 
2.1%
Other values (60)12970
22.7%

Length

2022-05-21T18:49:35.841484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of76569
 
7.2%
the64168
 
6.0%
and43161
 
4.1%
installations39128
 
3.7%
for35687
 
3.4%
landfills27084
 
2.5%
waste25579
 
2.4%
other23015
 
2.2%
or20368
 
1.9%
to19339
 
1.8%
Other values (347)690443
64.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FacilityInspireID
Categorical

HIGH CARDINALITY

Distinct7184
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
NL.RIVM/000000062.FACILITY
 
35
ES.CAED/003486000.FACILITY
 
34
AT.CAED/9008391228615.FACILITY
 
33
SE.CAED/10019434.Facility
 
32
UK.SEPA/200000018.Facility
 
30
Other values (7179)
56924 

Length

Max length96
Median length26
Mean length33.11214266
Min length17

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1167 ?
Unique (%)2.0%

Sample

1st rowhttps://registry.gdi-de.org/id/de.ni.mu/06221720040
2nd rowFR.EEA/4679.FACILITY
3rd rowLT.CAED/153009143.FACILITY
4th rowIT.CAED/810252002.FACILITY
5th rowRO.CAED/108BV0001.FACILITY

Common Values

ValueCountFrequency (%)
NL.RIVM/000000062.FACILITY35
 
0.1%
ES.CAED/003486000.FACILITY34
 
0.1%
AT.CAED/9008391228615.FACILITY33
 
0.1%
SE.CAED/10019434.Facility32
 
0.1%
UK.SEPA/200000018.Facility30
 
0.1%
EL.CAED/100037.FACILITY30
 
0.1%
https://data.ied_registry.omgeving.vlaanderen.be/id/productionfacility//BE.VL.000000605.FACILITY30
 
0.1%
UK.CAED/BEISOffsh-Foinaven-FPSO.FACILITY30
 
0.1%
FR.CAED/11416.FACILITY30
 
0.1%
FR.CAED/11428.FACILITY29
 
0.1%
Other values (7174)56775
99.5%

Length

2022-05-21T18:49:36.035977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nl.rivm/000000062.facility35
 
0.1%
es.caed/003486000.facility34
 
0.1%
at.caed/9008391228615.facility33
 
0.1%
se.caed/10019434.facility32
 
0.1%
https://data.ied_registry.omgeving.vlaanderen.be/id/productionfacility//be.vl.000000605.facility30
 
0.1%
uk.caed/beisoffsh-foinaven-fpso.facility30
 
0.1%
fr.caed/11416.facility30
 
0.1%
el.caed/100037.facility30
 
0.1%
uk.sepa/200000018.facility30
 
0.1%
fr.caed/11428.facility29
 
0.1%
Other values (7149)56775
99.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

facilityName
Categorical

HIGH CARDINALITY

Distinct7929
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
Enel Produzione S.p.A.
 
204
SNAM Rete Gas
 
108
Trans Austria Gasleitung GmbH
 
99
A2A gencogas S.p.A.
 
97
Versalis S.p.A.
 
89
Other values (7924)
56491 

Length

Max length152
Median length27
Mean length30.41402747
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1712 ?
Unique (%)3.0%

Sample

1st rowHolcim (Deutschland) GmbH Werk Höver
2nd rowHOLCIM (France) S.A.S - Usine de Dannes
3rd rowAB "Akmenės cementas"
4th rowCEMENTIR ITALIA S.p.A.
5th rowSC CRH CIMENT (ROMANIA) SA.

Common Values

ValueCountFrequency (%)
Enel Produzione S.p.A.204
 
0.4%
SNAM Rete Gas 108
 
0.2%
Trans Austria Gasleitung GmbH99
 
0.2%
A2A gencogas S.p.A.97
 
0.2%
Versalis S.p.A.89
 
0.2%
WIEN ENERGIE GmbH75
 
0.1%
Edison S.p.A.71
 
0.1%
Enipower S.p.A. 65
 
0.1%
Eni S.p.A. 59
 
0.1%
FERROPEM58
 
0.1%
Other values (7919)56163
98.4%

Length

2022-05-21T18:49:36.220969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8718
 
3.5%
de8358
 
3.4%
gmbh4678
 
1.9%
s.a3222
 
1.3%
di2606
 
1.1%
landfill2438
 
1.0%
power2194
 
0.9%
sa1785
 
0.7%
site1774
 
0.7%
s.p.a1709
 
0.7%
Other values (11130)208884
84.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

City
Categorical

HIGH CARDINALITY

Distinct5135
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
--
 
1713
Antwerpen
 
279
Duisburg
 
235
Cork
 
200
Botlek Rotterdam
 
194
Other values (5130)
54467 

Length

Max length47
Median length8
Mean length9.370042741
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique806 ?
Unique (%)1.4%

Sample

1st rowSehnde
2nd rowDANNES
3rd rowNaujoji Akmenė
4th rowMADDALONI
5th rowHoghiz

Common Values

ValueCountFrequency (%)
--1713
 
3.0%
Antwerpen279
 
0.5%
Duisburg235
 
0.4%
Cork200
 
0.4%
Botlek Rotterdam194
 
0.3%
FOS-SUR-MER145
 
0.3%
Gent140
 
0.2%
Bremen127
 
0.2%
Hamburg127
 
0.2%
Berlin125
 
0.2%
Other values (5125)53803
94.2%

Length

2022-05-21T18:49:36.385604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1737
 
2.4%
de1422
 
2.0%
la751
 
1.0%
rotterdam453
 
0.6%
san406
 
0.6%
st312
 
0.4%
antwerpen280
 
0.4%
del262
 
0.4%
duisburg235
 
0.3%
am207
 
0.3%
Other values (5512)65713
91.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

targetRelease
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
AIR
57088 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAIR
2nd rowAIR
3rd rowAIR
4th rowAIR
5th rowAIR

Common Values

ValueCountFrequency (%)
AIR57088
100.0%

Length

2022-05-21T18:49:36.511308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T18:49:36.587803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
air57088
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pollutant
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
Nitrogen oxides (NOX)
22619 
Carbon dioxide (CO2)
20017 
Methane (CH4)
14452 

Length

Max length21
Median length20
Mean length18.62414168
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarbon dioxide (CO2)
2nd rowNitrogen oxides (NOX)
3rd rowCarbon dioxide (CO2)
4th rowCarbon dioxide (CO2)
5th rowNitrogen oxides (NOX)

Common Values

ValueCountFrequency (%)
Nitrogen oxides (NOX)22619
39.6%
Carbon dioxide (CO2)20017
35.1%
Methane (CH4)14452
25.3%

Length

2022-05-21T18:49:36.782030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T18:49:36.868061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
nox22619
14.4%
oxides22619
14.4%
nitrogen22619
14.4%
co220017
12.8%
dioxide20017
12.8%
carbon20017
12.8%
ch414452
9.2%
methane14452
9.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

reportingYear
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.931457
Minimum2007
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size446.1 KiB
2022-05-21T18:49:36.948760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12010
median2013
Q32016
95-th percentile2019
Maximum2020
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.855553092
Coefficient of variation (CV)0.001915392141
Kurtosis-1.139770629
Mean2012.931457
Median Absolute Deviation (MAD)3
Skewness0.1238603607
Sum114914231
Variance14.86528965
MonotonicityNot monotonic
2022-05-21T18:49:37.061495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
20084638
 
8.1%
20104631
 
8.1%
20074608
 
8.1%
20094605
 
8.1%
20114466
 
7.8%
20124406
 
7.7%
20134386
 
7.7%
20144261
 
7.5%
20154129
 
7.2%
20164084
 
7.2%
Other values (4)12874
22.6%
ValueCountFrequency (%)
20074608
8.1%
20084638
8.1%
20094605
8.1%
20104631
8.1%
20114466
7.8%
20124406
7.7%
20134386
7.7%
20144261
7.5%
20154129
7.2%
20164084
7.2%
ValueCountFrequency (%)
20202096
3.7%
20193271
5.7%
20183479
6.1%
20174028
7.1%
20164084
7.2%
20154129
7.2%
20144261
7.5%
20134386
7.7%
20124406
7.7%
20114466
7.8%

MONTH
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.495182876
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size446.1 KiB
2022-05-21T18:49:37.191286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.444834059
Coefficient of variation (CV)0.5303675239
Kurtosis-1.213782271
Mean6.495182876
Median Absolute Deviation (MAD)3
Skewness-0.001242601267
Sum370797
Variance11.86688169
MonotonicityNot monotonic
2022-05-21T18:49:37.312525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
84845
8.5%
44839
8.5%
94828
8.5%
74797
8.4%
104782
8.4%
24771
8.4%
34767
8.4%
14728
8.3%
64722
8.3%
124710
8.3%
Other values (2)9299
16.3%
ValueCountFrequency (%)
14728
8.3%
24771
8.4%
34767
8.4%
44839
8.5%
54647
8.1%
64722
8.3%
74797
8.4%
84845
8.5%
94828
8.5%
104782
8.4%
ValueCountFrequency (%)
124710
8.3%
114652
8.1%
104782
8.4%
94828
8.5%
84845
8.5%
74797
8.4%
64722
8.3%
54647
8.1%
44839
8.5%
34767
8.4%

DAY
Real number (ℝ≥0)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.50134879
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size446.1 KiB
2022-05-21T18:49:37.445546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median14
Q322
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.087665719
Coefficient of variation (CV)0.5577181705
Kurtosis-1.20571931
Mean14.50134879
Median Absolute Deviation (MAD)7
Skewness-0.002614671055
Sum827853
Variance65.41033679
MonotonicityNot monotonic
2022-05-21T18:49:37.589985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
132134
 
3.7%
232122
 
3.7%
112113
 
3.7%
252112
 
3.7%
222111
 
3.7%
12101
 
3.7%
92088
 
3.7%
22077
 
3.6%
82073
 
3.6%
202055
 
3.6%
Other values (18)36102
63.2%
ValueCountFrequency (%)
12101
3.7%
22077
3.6%
32024
3.5%
41999
3.5%
51968
3.4%
62036
3.6%
71975
3.5%
82073
3.6%
92088
3.7%
102033
3.6%
ValueCountFrequency (%)
282022
3.5%
272021
3.5%
262003
3.5%
252112
3.7%
242050
3.6%
232122
3.7%
222111
3.7%
211997
3.5%
202055
3.6%
191950
3.4%

CONTINENT
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
EUROPE
57088 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEUROPE
2nd rowEUROPE
3rd rowEUROPE
4th rowEUROPE
5th rowEUROPE

Common Values

ValueCountFrequency (%)
EUROPE57088
100.0%

Length

2022-05-21T18:49:37.728290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T18:49:37.810557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
europe57088
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

max_wind_speed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57059
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.51871231
Minimum8.011957526
Maximum22.99138212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size446.1 KiB
2022-05-21T18:49:37.911613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8.011957526
5-th percentile10.36180949
Q113.3254658
median15.50697643
Q317.72034366
95-th percentile20.66908412
Maximum22.99138212
Range14.97942459
Interquartile range (IQR)4.394877861

Descriptive statistics

Standard deviation3.068112171
Coefficient of variation (CV)0.1977040433
Kurtosis-0.5889840794
Mean15.51871231
Median Absolute Deviation (MAD)2.198957946
Skewness-0.005163234779
Sum885932.2485
Variance9.413312291
MonotonicityNot monotonic
2022-05-21T18:49:38.093660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.981585152
 
< 0.1%
15.748336632
 
< 0.1%
19.201851132
 
< 0.1%
17.174740972
 
< 0.1%
12.373854092
 
< 0.1%
15.102554082
 
< 0.1%
12.731524342
 
< 0.1%
14.152453832
 
< 0.1%
13.59584232
 
< 0.1%
19.352357992
 
< 0.1%
Other values (57049)57068
> 99.9%
ValueCountFrequency (%)
8.0119575261
< 0.1%
8.060774021
< 0.1%
8.0626891721
< 0.1%
8.0802011441
< 0.1%
8.0956577041
< 0.1%
8.0960447781
< 0.1%
8.1058681341
< 0.1%
8.1079992031
< 0.1%
8.1374073041
< 0.1%
8.1461322551
< 0.1%
ValueCountFrequency (%)
22.991382121
< 0.1%
22.947671461
< 0.1%
22.946838651
< 0.1%
22.945513941
< 0.1%
22.941042321
< 0.1%
22.940007051
< 0.1%
22.930815991
< 0.1%
22.90957641
< 0.1%
22.901485811
< 0.1%
22.89694871
< 0.1%

avg_wind_speed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57063
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.01467035
Minimum14.00010009
Maximum21.99997338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size446.1 KiB
2022-05-21T18:49:38.286329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum14.00010009
5-th percentile14.39840244
Q116.01304102
median18.0169155
Q320.01130031
95-th percentile21.61810833
Maximum21.99997338
Range7.999873293
Interquartile range (IQR)3.99825929

Descriptive statistics

Standard deviation2.31176118
Coefficient of variation (CV)0.128326588
Kurtosis-1.196518668
Mean18.01467035
Median Absolute Deviation (MAD)1.9988536
Skewness-0.003505807287
Sum1028421.501
Variance5.344239752
MonotonicityNot monotonic
2022-05-21T18:49:38.473639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.849406022
 
< 0.1%
20.594837722
 
< 0.1%
19.332296122
 
< 0.1%
21.121406182
 
< 0.1%
21.293314422
 
< 0.1%
17.846496432
 
< 0.1%
18.903847352
 
< 0.1%
19.074180292
 
< 0.1%
19.631538462
 
< 0.1%
19.087465282
 
< 0.1%
Other values (57053)57068
> 99.9%
ValueCountFrequency (%)
14.000100091
< 0.1%
14.000287421
< 0.1%
14.000375591
< 0.1%
14.000387131
< 0.1%
14.000398591
< 0.1%
14.000403841
< 0.1%
14.000404271
< 0.1%
14.000473281
< 0.1%
14.000633421
< 0.1%
14.000726781
< 0.1%
ValueCountFrequency (%)
21.999973381
< 0.1%
21.999919471
< 0.1%
21.999890791
< 0.1%
21.999874691
< 0.1%
21.999569731
< 0.1%
21.999455531
< 0.1%
21.999331351
< 0.1%
21.999256291
< 0.1%
21.998791
< 0.1%
21.998742931
< 0.1%

min_wind_speed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57063
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.51937642
Minimum15.03258912
Maximum29.93360301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size446.1 KiB
2022-05-21T18:49:38.630264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15.03258912
5-th percentile17.37047692
Q120.33513287
median22.53624822
Q324.71464892
95-th percentile27.60255311
Maximum29.93360301
Range14.90101389
Interquartile range (IQR)4.379516043

Descriptive statistics

Standard deviation3.060383387
Coefficient of variation (CV)0.1359000058
Kurtosis-0.5934916153
Mean22.51937642
Median Absolute Deviation (MAD)2.188979446
Skewness-0.01786328298
Sum1285586.161
Variance9.365946477
MonotonicityNot monotonic
2022-05-21T18:49:38.780046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.113882492
 
< 0.1%
23.65737752
 
< 0.1%
22.724596612
 
< 0.1%
28.881635432
 
< 0.1%
28.380285892
 
< 0.1%
27.108921292
 
< 0.1%
21.04516862
 
< 0.1%
26.914441342
 
< 0.1%
24.410366252
 
< 0.1%
17.876777422
 
< 0.1%
Other values (57053)57068
> 99.9%
ValueCountFrequency (%)
15.032589121
< 0.1%
15.045357621
< 0.1%
15.053131441
< 0.1%
15.055647381
< 0.1%
15.05953471
< 0.1%
15.068568541
< 0.1%
15.080219391
< 0.1%
15.101682161
< 0.1%
15.116900611
< 0.1%
15.118411791
< 0.1%
ValueCountFrequency (%)
29.933603011
< 0.1%
29.925566921
< 0.1%
29.914366771
< 0.1%
29.906585941
< 0.1%
29.904341731
< 0.1%
29.898358661
< 0.1%
29.89587811
< 0.1%
29.888961061
< 0.1%
29.875862671
< 0.1%
29.869648491
< 0.1%

max_temp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57058
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.457074188
Minimum-3.141463865
Maximum20.93826591
Zeros0
Zeros (%)0.0%
Negative2388
Negative (%)4.2%
Memory size446.1 KiB
2022-05-21T18:49:38.943917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.141463865
5-th percentile0.3108750329
Q15.891963107
median9.689104532
Q313.27769263
95-th percentile17.72783091
Maximum20.93826591
Range24.07972977
Interquartile range (IQR)7.385729525

Descriptive statistics

Standard deviation5.211227426
Coefficient of variation (CV)0.5510401338
Kurtosis-0.6672614422
Mean9.457074188
Median Absolute Deviation (MAD)3.68127557
Skewness-0.1683652136
Sum539885.4513
Variance27.15689129
MonotonicityNot monotonic
2022-05-21T18:49:39.237478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5028111372
 
< 0.1%
9.1247101282
 
< 0.1%
10.055839952
 
< 0.1%
17.394731672
 
< 0.1%
9.1592080432
 
< 0.1%
2.679637912
 
< 0.1%
3.3421996412
 
< 0.1%
12.400744942
 
< 0.1%
7.2266327662
 
< 0.1%
10.799053552
 
< 0.1%
Other values (57048)57068
> 99.9%
ValueCountFrequency (%)
-3.1414638651
< 0.1%
-3.0755626211
< 0.1%
-3.0715252091
< 0.1%
-3.0414524951
< 0.1%
-3.0331780121
< 0.1%
-2.9574472361
< 0.1%
-2.9470228231
< 0.1%
-2.939820011
< 0.1%
-2.9391647231
< 0.1%
-2.928912211
< 0.1%
ValueCountFrequency (%)
20.938265911
< 0.1%
20.926115881
< 0.1%
20.855884991
< 0.1%
20.855570111
< 0.1%
20.847543571
< 0.1%
20.843564551
< 0.1%
20.840629251
< 0.1%
20.837208761
< 0.1%
20.82433531
< 0.1%
20.819539631
< 0.1%

avg_temp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57059
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.4516403
Minimum-0.1991759675
Maximum19.99940286
Zeros0
Zeros (%)0.0%
Negative346
Negative (%)0.6%
Memory size446.1 KiB
2022-05-21T18:49:39.386754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.1991759675
5-th percentile1.325644151
Q17.191469317
median10.69063333
Q314.19475221
95-th percentile18.6832901
Maximum19.99940286
Range20.19857883
Interquartile range (IQR)7.003282891

Descriptive statistics

Standard deviation5.079899246
Coefficient of variation (CV)0.4860384685
Kurtosis-0.7260446631
Mean10.4516403
Median Absolute Deviation (MAD)3.50111851
Skewness-0.1851573852
Sum596663.2416
Variance25.80537635
MonotonicityNot monotonic
2022-05-21T18:49:39.539930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65960183442
 
< 0.1%
14.26679232
 
< 0.1%
11.29314612
 
< 0.1%
11.084730472
 
< 0.1%
10.466804182
 
< 0.1%
13.069060752
 
< 0.1%
8.9109201142
 
< 0.1%
5.6314035242
 
< 0.1%
19.147482582
 
< 0.1%
7.3290073972
 
< 0.1%
Other values (57049)57068
> 99.9%
ValueCountFrequency (%)
-0.19917596751
< 0.1%
-0.19865665941
< 0.1%
-0.1985739161
< 0.1%
-0.19794725681
< 0.1%
-0.19563173551
< 0.1%
-0.19502971571
< 0.1%
-0.19436259071
< 0.1%
-0.19430993031
< 0.1%
-0.1938190561
< 0.1%
-0.1937039161
< 0.1%
ValueCountFrequency (%)
19.999402861
< 0.1%
19.999342571
< 0.1%
19.998710251
< 0.1%
19.998645121
< 0.1%
19.998587871
< 0.1%
19.998520631
< 0.1%
19.998478841
< 0.1%
19.998339361
< 0.1%
19.997677011
< 0.1%
19.997626721
< 0.1%

min_temp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57062
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.44788779
Minimum0.8948269078
Maximum24.902108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size446.1 KiB
2022-05-21T18:49:39.682549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.8948269078
5-th percentile4.251666344
Q19.90879549
median13.68118658
Q317.2723975
95-th percentile21.74360624
Maximum24.902108
Range24.00728109
Interquartile range (IQR)7.363602007

Descriptive statistics

Standard deviation5.211141584
Coefficient of variation (CV)0.3875063256
Kurtosis-0.6549770834
Mean13.44788779
Median Absolute Deviation (MAD)3.672338715
Skewness-0.1680304555
Sum767713.0182
Variance27.15599661
MonotonicityNot monotonic
2022-05-21T18:49:39.815436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8570305462
 
< 0.1%
22.259320422
 
< 0.1%
20.117502382
 
< 0.1%
10.319728432
 
< 0.1%
9.7858551282
 
< 0.1%
24.142557542
 
< 0.1%
13.137713212
 
< 0.1%
11.90244222
 
< 0.1%
19.567566722
 
< 0.1%
11.819700872
 
< 0.1%
Other values (57052)57068
> 99.9%
ValueCountFrequency (%)
0.89482690781
< 0.1%
0.89595217821
< 0.1%
0.95077708141
< 0.1%
0.99527312051
< 0.1%
0.99992670431
< 0.1%
1.0036063931
< 0.1%
1.0098082281
< 0.1%
1.0115172011
< 0.1%
1.0219680871
< 0.1%
1.0336835231
< 0.1%
ValueCountFrequency (%)
24.9021081
< 0.1%
24.884842111
< 0.1%
24.855425061
< 0.1%
24.848932621
< 0.1%
24.84067121
< 0.1%
24.825793291
< 0.1%
24.825372931
< 0.1%
24.820613351
< 0.1%
24.817791821
< 0.1%
24.812788731
< 0.1%

DAY WITH FOGS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.23360426
Minimum0
Maximum19
Zeros16355
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size446.1 KiB
2022-05-21T18:49:39.929899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile12
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.777178511
Coefficient of variation (CV)1.691068816
Kurtosis7.910063552
Mean2.23360426
Median Absolute Deviation (MAD)1
Skewness2.903021266
Sum127512
Variance14.26707751
MonotonicityNot monotonic
2022-05-21T18:49:40.056045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
116967
29.7%
216734
29.3%
016355
28.6%
11456
 
0.8%
5447
 
0.8%
15433
 
0.8%
3433
 
0.8%
18433
 
0.8%
4427
 
0.7%
12425
 
0.7%
Other values (10)3978
 
7.0%
ValueCountFrequency (%)
016355
28.6%
116967
29.7%
216734
29.3%
3433
 
0.8%
4427
 
0.7%
5447
 
0.8%
6383
 
0.7%
7382
 
0.7%
8414
 
0.7%
9414
 
0.7%
ValueCountFrequency (%)
19413
0.7%
18433
0.8%
17391
0.7%
16408
0.7%
15433
0.8%
14409
0.7%
13344
0.6%
12425
0.7%
11456
0.8%
10420
0.7%

REPORTER NAME
Categorical

HIGH CARDINALITY
UNIFORM

Distinct45014
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
James Smith
 
23
Michael Smith
 
23
Michael Brown
 
21
Christopher Smith
 
18
David Smith
 
18
Other values (45009)
56985 

Length

Max length28
Median length13
Mean length13.27590737
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37898 ?
Unique (%)66.4%

Sample

1st rowMr. Jacob Ortega
2nd rowAndrew Tran
3rd rowMichael Mendoza
4th rowJacqueline Ramirez
5th rowGerald Ponce

Common Values

ValueCountFrequency (%)
James Smith23
 
< 0.1%
Michael Smith23
 
< 0.1%
Michael Brown21
 
< 0.1%
Christopher Smith18
 
< 0.1%
David Smith18
 
< 0.1%
Jennifer Smith18
 
< 0.1%
Jennifer Johnson18
 
< 0.1%
Jessica Smith17
 
< 0.1%
Robert Jones17
 
< 0.1%
Michael Johnson17
 
< 0.1%
Other values (45004)56898
99.7%

Length

2022-05-21T18:49:40.203571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michael1302
 
1.1%
smith1189
 
1.0%
johnson996
 
0.9%
james952
 
0.8%
david853
 
0.7%
christopher832
 
0.7%
john831
 
0.7%
jennifer797
 
0.7%
williams792
 
0.7%
robert789
 
0.7%
Other values (1588)107434
92.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CITY ID
Categorical

HIGH CARDINALITY

Distinct5135
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
cfab1ba8c67c7c838db98d666f02a132
 
1713
aed13ea855ff8b71cd5ceb869fe744c1
 
279
f53da95e5700ca1e7d12b7a833d62663
 
235
002c887b8369e59e6f58a5d06a8d0817
 
200
0759b751086c80f98aa59e11e6a115b4
 
194
Other values (5130)
54467 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique806 ?
Unique (%)1.4%

Sample

1st row7cdb5e74adcb2ffaa21c1b61395a984f
2nd row63f9745320f57c7a1f2c1e6e18cedc6d
3rd rowc3e198d3cce7e1a8c55db94e11b6ee8b
4th row74d9cf54417f8c7cbde9b8dcc2b70671
5th rowdcfb06f6c717e109e530d64f2d09f559

Common Values

ValueCountFrequency (%)
cfab1ba8c67c7c838db98d666f02a1321713
 
3.0%
aed13ea855ff8b71cd5ceb869fe744c1279
 
0.5%
f53da95e5700ca1e7d12b7a833d62663235
 
0.4%
002c887b8369e59e6f58a5d06a8d0817200
 
0.4%
0759b751086c80f98aa59e11e6a115b4194
 
0.3%
8dc3d6c792dfa6e7eb4c59921e6c635a145
 
0.3%
bc1f8a8dc753022dcebc810482590fdd140
 
0.2%
35d7df6ed3d93be2927d14acc5f1fc9a127
 
0.2%
92c1f80a07ad537ddb7e00137d6a25f9127
 
0.2%
ee1611b61f5688e70c12b40684dbb395125
 
0.2%
Other values (5125)53803
94.2%

Length

2022-05-21T18:49:40.315394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cfab1ba8c67c7c838db98d666f02a1321713
 
3.0%
aed13ea855ff8b71cd5ceb869fe744c1279
 
0.5%
f53da95e5700ca1e7d12b7a833d62663235
 
0.4%
002c887b8369e59e6f58a5d06a8d0817200
 
0.4%
0759b751086c80f98aa59e11e6a115b4194
 
0.3%
8dc3d6c792dfa6e7eb4c59921e6c635a145
 
0.3%
bc1f8a8dc753022dcebc810482590fdd140
 
0.2%
35d7df6ed3d93be2927d14acc5f1fc9a127
 
0.2%
92c1f80a07ad537ddb7e00137d6a25f9127
 
0.2%
ee1611b61f5688e70c12b40684dbb395125
 
0.2%
Other values (5125)53803
94.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EPRTRSectorCode
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.174940443
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size446.1 KiB
2022-05-21T18:49:40.403415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.051621838
Coefficient of variation (CV)0.646192228
Kurtosis-0.9324922212
Mean3.174940443
Median Absolute Deviation (MAD)2
Skewness0.4184316815
Sum181251
Variance4.209152168
MonotonicityNot monotonic
2022-05-21T18:49:40.497552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
121386
37.5%
513813
24.2%
38922
15.6%
43744
 
6.6%
63299
 
5.8%
22748
 
4.8%
71848
 
3.2%
81120
 
2.0%
9208
 
0.4%
ValueCountFrequency (%)
121386
37.5%
22748
 
4.8%
38922
15.6%
43744
 
6.6%
513813
24.2%
63299
 
5.8%
71848
 
3.2%
81120
 
2.0%
9208
 
0.4%
ValueCountFrequency (%)
9208
 
0.4%
81120
 
2.0%
71848
 
3.2%
63299
 
5.8%
513813
24.2%
43744
 
6.6%
38922
15.6%
22748
 
4.8%
121386
37.5%

EPRTRAnnexIMainActivityCode
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct70
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
1(c)
18780 
5(d)
9028 
5(b)
3039 
3(c)(i)
2907 
3(e)
2367 
Other values (65)
20967 

Length

Max length10
Median length4
Mean length4.552007427
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3(c)(i)
2nd row3(c)(i)
3rd row3(c)(i)
4th row3(c)(i)
5th row3(c)(i)

Common Values

ValueCountFrequency (%)
1(c)18780
32.9%
5(d)9028
15.8%
5(b)3039
 
5.3%
3(c)(i)2907
 
5.1%
3(e)2367
 
4.1%
1(a)2110
 
3.7%
6(b)2072
 
3.6%
3(c)1326
 
2.3%
2(b)1268
 
2.2%
6(a)1221
 
2.1%
Other values (60)12970
22.7%

Length

2022-05-21T18:49:40.610527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1(c18780
32.9%
5(d9028
15.8%
5(b3039
 
5.3%
3(c)(i2907
 
5.1%
3(e2367
 
4.1%
1(a2110
 
3.7%
6(b2072
 
3.6%
3(c1326
 
2.3%
2(b1268
 
2.2%
6(a1221
 
2.1%
Other values (60)12970
22.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Unnamed: 23
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct28501
Distinct (%)100.0%
Missing28587
Missing (%)50.1%
Infinite0
Infinite (%)0.0%
Mean40836.98646
Minimum0
Maximum81594
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size446.1 KiB
2022-05-21T18:49:40.739600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4041
Q120308
median40768
Q361272
95-th percentile77513
Maximum81594
Range81594
Interquartile range (IQR)40964

Descriptive statistics

Standard deviation23584.38694
Coefficient of variation (CV)0.5775251552
Kurtosis-1.206297706
Mean40836.98646
Median Absolute Deviation (MAD)20481
Skewness-0.006153988305
Sum1163894951
Variance556223307.4
MonotonicityNot monotonic
2022-05-21T18:49:40.902322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80951
 
< 0.1%
653421
 
< 0.1%
587101
 
< 0.1%
180721
 
< 0.1%
67031
 
< 0.1%
397211
 
< 0.1%
337971
 
< 0.1%
764611
 
< 0.1%
117171
 
< 0.1%
576131
 
< 0.1%
Other values (28491)28491
49.9%
(Missing)28587
50.1%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
71
< 0.1%
151
< 0.1%
191
< 0.1%
201
< 0.1%
251
< 0.1%
261
< 0.1%
271
< 0.1%
281
< 0.1%
ValueCountFrequency (%)
815941
< 0.1%
815791
< 0.1%
815781
< 0.1%
815771
< 0.1%
815691
< 0.1%
815651
< 0.1%
815641
< 0.1%
815621
< 0.1%
815611
< 0.1%
815571
< 0.1%

Unnamed: 0
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing57088
Missing (%)100.0%
Memory size446.1 KiB

pollutant_code
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size446.1 KiB
0
22619 
1
20017 
2
14452 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
022619
39.6%
120017
35.1%
214452
25.3%

Length

2022-05-21T18:49:41.042783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T18:49:41.107602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
022619
39.6%
120017
35.1%
214452
25.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-21T18:49:31.258098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:08.670804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:10.990613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:12.973592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:14.980156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:17.313364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:19.317101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:21.351090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:23.173985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:25.292535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:27.151583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:29.329517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:31.412496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:08.890199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:11.131592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:13.148763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:15.135911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:17.476806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:19.484177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:21.504762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:23.334709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:25.449965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:27.309767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:29.488842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:31.561084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:09.082225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:11.269434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:13.305966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:15.467930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:17.643677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:19.634241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:21.650143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:23.499249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:25.595701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:27.467218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:29.649122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:31.702322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:09.268408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:11.423366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:13.456766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:15.621069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:17.800853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:19.905711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:21.794050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:23.654255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:25.741087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:27.613195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:29.794574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:31.859036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:09.433329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:11.581075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:13.619990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:15.806879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:18.003136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:20.069645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:21.950398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:23.816823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:25.913057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:27.775204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:29.960885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:32.025964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:09.608741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:11.726852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:13.806846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:15.981150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:18.176140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:20.231373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:22.103723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:24.103205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:26.093405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:27.959642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:30.121389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:32.186188image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:09.779287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:11.915637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:13.968279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:16.158913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:18.337148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:20.391919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:22.255937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:24.283518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:26.253127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:28.147514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:30.315058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:32.331187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:09.940181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:12.080597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:14.114741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:16.460681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:18.491582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:20.552355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:22.391059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:24.440462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:26.386020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:28.447006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:30.462116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:32.608574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:10.122139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:12.248821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:14.265800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:16.636082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:18.672777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:20.721677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:22.555024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:24.625295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:26.543091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:28.653990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:30.628413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:32.763172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:10.271116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:12.424493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:14.436967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:16.801843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:18.829362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:20.877765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:22.703472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:24.776542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:26.690495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:28.829262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:30.785272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:32.914266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:10.522998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:12.623809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:14.631738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:16.995636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:18.997363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:21.040026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:22.864018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:24.944567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:26.845816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:29.007769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:30.941880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:33.076759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:10.804181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:12.788351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:14.791728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:17.154759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:19.147292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:21.191652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:23.009738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:25.115038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:26.996432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:29.163170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-21T18:49:31.095066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-05-21T18:49:41.199628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-21T18:49:41.515515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-21T18:49:41.741528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-21T18:49:41.962660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-21T18:49:42.157594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-21T18:49:33.418105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-21T18:49:34.463845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-21T18:49:34.893402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

countryNameeprtrSectorNameEPRTRAnnexIMainActivityLabelFacilityInspireIDfacilityNameCitytargetReleasepollutantreportingYearMONTHDAYCONTINENTmax_wind_speedavg_wind_speedmin_wind_speedmax_tempavg_tempmin_tempDAY WITH FOGSREPORTER NAMECITY IDEPRTRSectorCodeEPRTRAnnexIMainActivityCodeUnnamed: 23Unnamed: 0pollutant_code
0GermanyMineral industryInstallations for the production of cement clinker in rotary kilnshttps://registry.gdi-de.org/id/de.ni.mu/06221720040Holcim (Deutschland) GmbH Werk HöverSehndeAIRCarbon dioxide (CO2)20151020EUROPE15.11876714.31254121.4191062.8648954.9241699.6882062Mr. Jacob Ortega7cdb5e74adcb2ffaa21c1b61395a984f33(c)(i)NaNNaN1
1FranceMineral industryInstallations for the production of cement clinker in rotary kilnsFR.EEA/4679.FACILITYHOLCIM (France) S.A.S - Usine de DannesDANNESAIRNitrogen oxides (NOX)200928EUROPE22.18205921.81605125.8694463.1340496.0001908.6452090Andrew Tran63f9745320f57c7a1f2c1e6e18cedc6d33(c)(i)NaNNaN0
2LithuaniaMineral industryInstallations for the production of cement clinker in rotary kilnsLT.CAED/153009143.FACILITYAB "Akmenės cementas"Naujoji AkmenėAIRCarbon dioxide (CO2)201339EUROPE13.80700714.43245517.63760314.90155317.01995718.1030461Michael Mendozac3e198d3cce7e1a8c55db94e11b6ee8b33(c)(i)NaNNaN1
3ItalyMineral industryInstallations for the production of cement clinker in rotary kilnsIT.CAED/810252002.FACILITYCEMENTIR ITALIA S.p.A.MADDALONIAIRCarbon dioxide (CO2)2011924EUROPE15.47911316.13556318.87385714.65515513.90740915.0121761Jacqueline Ramirez74d9cf54417f8c7cbde9b8dcc2b7067133(c)(i)NaNNaN1
4RomaniaMineral industryInstallations for the production of cement clinker in rotary kilnsRO.CAED/108BV0001.FACILITYSC CRH CIMENT (ROMANIA) SA.HoghizAIRNitrogen oxides (NOX)2016121EUROPE11.23708114.38326922.07006112.20962613.30482817.1732331Gerald Poncedcfb06f6c717e109e530d64f2d09f55933(c)(i)NaNNaN0
5SpainMineral industryInstallations for the production of cement clinker in rotary kilnsES.CAED/002072000.FACILITYFABRICA DE MATAPORQUERA (CEMENTOS ALFA)MATAPORQUERAAIRNitrogen oxides (NOX)2019721EUROPE20.53907919.63959526.48536916.05426716.65600619.3423252Summer Potts60d05227eb203b63d7220797fa4ab46633(c)(i)NaNNaN0
6SwitzerlandMineral industryInstallations for the production of cement clinker in rotary kilnsCH.CAED/000000178.FacilityJuracime SACornauxAIRNitrogen oxides (NOX)2013214EUROPE18.76152820.48977224.31629015.07881914.84950118.0184771Manuel Fox258546a3be5e42182debfd2c8bfb3ada33(c)(i)NaNNaN0
7BulgariaMineral industryInstallations for the production of cement clinker in rotary kilnsBG.EEA/15805.FACILITYVULKANDimitrovgradAIRNitrogen oxides (NOX)20071027EUROPE14.97341015.27723922.90948910.1441539.35317611.0686280Lori Garcia7915da14382d06785bdd501ed7eea6e033(c)(i)NaNNaN0
8FranceMineral industryInstallations for the production of cement clinker in rotary kilnsFR.CAED/11830.FACILITYCIMENTS CALCIA - SITE DE BUSSACBUSSAC-FORETAIRNitrogen oxides (NOX)2014119EUROPE12.42014114.09817617.4294853.2540873.1151006.2238051Travis Fisher6cc5a230870ae4fce13e2c4f53ad113333(c)(i)NaNNaN0
9FranceMineral industryInstallations for the production of cement clinker in rotary kilnsFR.CAED/11830.FACILITYCIMENTS CALCIA - SITE DE BUSSACBUSSAC-FORETAIRCarbon dioxide (CO2)20101024EUROPE19.27923918.70798725.6757730.6234440.1456343.6064521Angela Riley6cc5a230870ae4fce13e2c4f53ad113333(c)(i)NaNNaN1

Last rows

countryNameeprtrSectorNameEPRTRAnnexIMainActivityLabelFacilityInspireIDfacilityNameCitytargetReleasepollutantreportingYearMONTHDAYCONTINENTmax_wind_speedavg_wind_speedmin_wind_speedmax_tempavg_tempmin_tempDAY WITH FOGSREPORTER NAMECITY IDEPRTRSectorCodeEPRTRAnnexIMainActivityCodeUnnamed: 23Unnamed: 0pollutant_code
57078FranceEnergy sectorThermal power stations and other combustion installationsFR.CAED/12044.FACILITYEDF PRODUCTION ELECTRIQUE INSULAIRE - ETABLISSEMENT DE HAUTE CORSELUCCIANAAIRNitrogen oxides (NOX)2016524EUROPE12.09865017.01842323.97391817.98069719.24789322.7812760Kimberly Taylor495a606d3f1402613349b0c95d35d93111(c)18066.0NaN0
57079ItalyWaste and wastewater managementLandfills (excluding landfills of inert waste and landfills, which were definitely closed before 16.7.2001 or for which the after-care phase required by the competent authorities according to Article 13 of Council Directive 1999/31/EC of 26 April 1999 on the landfill of waste has expired)IT.EEA/104.FACILITYDiscarica di Barengo (NO)BARENGOAIRMethane (CH4)2017226EUROPE18.43199820.52847826.1403988.1453848.35749110.0966470Colin Hammond7e0cee13d05d1d0ea4ca0973fcc1bf7d55(d)42629.0NaN2
57080FranceMineral industryInstallations for the manufacture of glass, including glass fibreFR.CAED/10710.FACILITYARC FRANCE - SITE D'ARQUESARQUESAIRCarbon dioxide (CO2)20071217EUROPE14.32847919.97490125.6389292.7444282.6347645.2522931Madison Jackson45f325609b3242ae51996742cacb606e33(e)11953.0NaN1
57081ItalyWaste and wastewater managementLandfills (excluding landfills of inert waste and landfills, which were definitely closed before 16.7.2001 or for which the after-care phase required by the competent authorities according to Article 13 of Council Directive 1999/31/EC of 26 April 1999 on the landfill of waste has expired)IT.EEA/115315.FACILITYMANDURIAMBIENTE S.p.A.MANDURIAAIRMethane (CH4)20161023EUROPE16.41231017.42104419.31772211.32108613.72942716.2321190Kimberly Scott3d508ddbc66ac3b45f01e5c7b191619e55(d)42042.0NaN2
57082SerbiaChemical industryChemical installations for the production on an industrial scale of basic organic chemicals: Oxygen-containing hydrocarbons such as alcohols, aldehydes, ketones, carboxylic acids, esters, acetates, ethers, peroxides, epoxy resinsRS.SEPA.NRIZ/FACILITY.000000116MSK postrojenjeKikindaAIRNitrogen oxides (NOX)2019811EUROPE15.71970116.40820222.31166610.65043511.02268315.8258242Francisco Wilsonffdce8563b060038d08b880c452d042e44(a)(ii)56922.0NaN0
57083CyprusEnergy sectorThermal power stations and other combustion installationsCY.CAED/0030030000.FACILITYElectricity Authority of Cyprus, Vassilikos Power StationLARNAKAAIRCarbon dioxide (CO2)200811EUROPE13.47598818.55647622.85253013.34580112.41078317.1483270Tammy Faulkner2d4776365b33d5f1be53ea4606e2c79c11(c)5147.0NaN1
57084FinlandEnergy sectorThermal power stations and other combustion installationshttp://paikkatiedot.fi/so/1002031/pf/ProductionFacility/0000001728.ProductionFacilityTurun Seudun Energiantuotanto Oy, Naantalin voimalaitosNaantaliAIRNitrogen oxides (NOX)20081219EUROPE8.81593914.46170320.5537813.8202813.7638335.6571070Dr. Courtney Bryant020b11bf06b96aae1dd910a56674a8aa11(c)9442.0NaN0
57085SloveniaWaste and wastewater managementLandfills (excluding landfills of inert waste and landfills, which were definitely closed before 16.7.2001 or for which the after-care phase required by the competent authorities according to Article 13 of Council Directive 1999/31/EC of 26 April 1999 on the landfill of waste has expired)SI.ARSO/000000037.FACILITYJavne službe Ptuj, Odlagališče nenevarnih odpadkov GajkePtujAIRMethane (CH4)2010810EUROPE14.79329816.68804920.41149817.28536518.34979821.5384412William Greer84afdc8367dfd9124e8b8f994e986fe955(d)57189.0NaN2
57086ItalyMineral industryUnderground mining and related operationsIT.CAED/850592002.FACILITYCentro Olio Val d'AgriVIGGIANOAIRNitrogen oxides (NOX)2014125EUROPE14.91131716.14409122.6471926.3871996.1762389.2690760Leonard Roberts09ad69bcf41256f40be3314a33e0438c33(a)40953.0NaN0
57087United KingdomEnergy sectorThermal power stations and other combustion installationsGB.EEA/13394.FACILITYSSE Generation Ltd, Weston Point Salt Works CHP PantRuncornAIRCarbon dioxide (CO2)2008723EUROPE21.76181221.29694929.2482768.22067811.19430814.17178013Mr. Benjamin Parkb5f44c55c14c881ea21499a32fc972d011(c)71260.0NaN1